Explainable Artificial Intelligence (2025) (Mohamed Medhat Gaber)

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8365291126 Mohamed Medhat Gaber Scientific Foundation SmarterPoland.pl 2025

Catergory: Computer Technology, Nonfiction

Unlock the Black Box: Master the Art and Science of Explainable AI
In an era where artificial intelligence systems make critical decisions affecting everything from medical diagnoses to financial loans, the ability to understand and explain AI behavior has become not just important-it's essential. This comprehensive guide provides the definitive resource for anyone seeking to master the rapidly evolving field of Explainable Artificial Intelligence (XAI).
What You'll Discover:
🎯 Complete Theoretical Foundation

✔ Mathematical principles underlying explanation methods, from information theory to game theory
✔ Deep dive into Shapley values, gradient-based attribution, and causal inference
✔ Comprehensive taxonomy of explanation approaches and their applications

⚡ Practical Implementation Guidance

✔ Step-by-step tutorials on LIME, SHAP, and other leading XAI techniques
✔ Real-world case studies across healthcare, finance, legal systems, and autonomous vehicles
✔ Production-ready strategies for building scalable explainable AI systems

🔬 Advanced Methods and Evaluation

✔ Model-specific interpretability techniques for neural networks, tree ensembles, and more
✔ Rigorous evaluation frameworks including human-centered assessment
✔ Cutting-edge approaches to handle multi-modal data and adversarial robustness

🏗️ From Research to Production

✔ Software architecture patterns for explainable AI systems
✔ Performance optimization and caching strategies
✔ Governance, compliance, and quality assurance frameworks

Why This Book Stands Apart:
Unlike other AI books that treat explainability as an afterthought, this volume places interpretability at the center of AI system design. It bridges the gap between academic research and practical implementation, providing both the theoretical depth needed for research and the practical guidance required for real-world deployment.
Perfect For:

✔ AI/ML Researchers seeking comprehensive coverage of XAI methods and evaluation techniques
✔ Data Scientists and Engineers implementing explainable systems in production environments
✔ Graduate Students studying machine learning, AI ethics, or human-computer interaction
✔ Business Leaders navigating regulatory requirements and building trustworthy AI systems
✔ Practitioners in high-stakes domains like healthcare, finance, and legal technology

What Sets This Apart:

✔ Eight comprehensive chapters covering everything from mathematical foundations to future research directions
✔ Extensive bibliography with over 200 references to cutting-edge research
✔ Practical code examples and implementation guidance
✔ Real-world case studies from industry leaders
✔ Forward-looking perspective on emerging trends and challenges

Master the Future of AI:
As AI systems become more sophisticated and ubiquitous, the demand for explainable solutions continues to grow exponentially. Regulatory frameworks like GDPR, ethical AI initiatives, and the need for human-AI collaboration all drive the importance of interpretable systems.
This book doesn't just teach you about explainable AI - it prepares you to lead in a field where transparency, accountability, and trust are paramount. Whether you're building diagnostic systems that doctors can trust, financial models that regulators can audit, or recommendation systems that users can understand, this comprehensive guide provides the knowledge and tools you need to succeed.
Transform your understanding of AI from black box to clear box. Start building trustworthy, interpretable AI systems today.

Contents of Download:
📌 Lioutikov R. Explainable Artificial Intelligence 2025.pdf (Mohamed Medhat Gaber) (2025) (108.86 MB)

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⭐️ Explainable Artificial Intelligence (2025) ✅ (108.86 MB)
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